We evaluate the performance of FedLAMA across three representative image classification benchmark datasets: CIFAR-10 ([1]}), CIFAR-100, and Federated Extended MNIST ([2]}).
Our experimental results deliver novel insights on how to aggregate the local models efficiently consuming the network bandwidth.
Given a fixed number of training iterations, as the aggregation interval increases, FedLAMA reduces the communication cost by up to \(60\%\)  under IID data settings and \(70\%\)  under non-IID data settings, while having only a marginal accuracy drop.
